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Detecting frequency-dependent selection through the effects of genotype similarity on fitness components

Cite this dataset

Sato, Yasuhiro; Takahashi, Yuma; Xu, Chongmeng; Shimizu, Kentaro K. (2023). Detecting frequency-dependent selection through the effects of genotype similarity on fitness components [Dataset]. Dryad. https://doi.org/10.5061/dryad.zs7h44jdv

Abstract

Frequency-dependent selection (FDS) is an evolutionary regime that can maintain or reduce polymorphisms. Despite the increasing availability of polymorphism data, few effective methods are available for estimating the gradient of FDS from the observed fitness components. We modeled the effects of genotype similarity on individual fitness to develop a selection gradient analysis of FDS. This modeling enabled us to estimate FDS by regressing fitness components on the genotype similarity among individuals. We detected known negative FDS on the visible polymorphism in a wild Arabidopsis and damselfly by applying this analysis to single-locus data. Further, we simulated genome-wide polymorphisms and fitness components to modify the single-locus analysis as a genome-wide association study (GWAS). The simulation showed that negative or positive FDS could be distinguished through the estimated effects of genotype similarity on simulated fitness. Moreover, we conducted the GWAS of the reproductive branch number in Arabidopsis thaliana and found that negative FDS was enriched among the top-associated polymorphisms of FDS. These results showed the potential applicability of the proposed method for FDS on both visible polymorphism and genome-wide polymorphisms. Overall, our study provides an effective method for selection gradient analysis to understand the maintenance or loss of polymorphism.

README: Regression model of frequency-dependent selection

Source codes and data of Sato et al. "Detecting frequency-dependent selection through the effects of genotype similarity on fitness components". A vignette-style instruction is available in Rmarkdown and its output HTML as index.Rmd and index.html, respectively (see also https://yassato.github.io/RegressionFDS/ for viewing). A developer version is available at https://github.com/yassato/RegressionFDS.

application

This directory includes source codes and/or input data for empirical analyses on Arabidopsis halleri, Ischnura elegans, and Arabidopsis thaliana, proceeding as follows.

GLMM analysis of Arabidopsis halleri

  • Ahal_FieldSurvey.R\ Poisson GLMM of the flower number in wild Arabidopsis halleri in a natural population. Data are available via Dryad repository (https://doi.org/10.5061/dryad.53k2d).

GLMM analysis of Ischnura elegans

  • damselfly_cage_data.R\ Poisson GLMM of the egg number in Ischnura elegans in semi-field cages. The original data are generated by Takahashi et al. (2014) Nature Communications 5:4468.

Branch number GWAS in Arabidopsis thaliana

  • pheno_branchNum.csv\
    Accession list and phenotype data as an input file for "BranchNo2019GWAS.R", including the following columns.

    • IndivID: Plant ID
    • Name: Name of the accession
    • Source: CS number of ABRC seed stock
    • gwasID: GWAS genotype ID assigned to the accession
    • Block: Experimental block ID
    • position_X: cell-tray position along x-axis within a block
    • position_Y: cell-tray position along y-axis within a block
    • edge: Edge of the block (1) or not (0).
    • InitLeafLen: The length of largest leaf at the start of field experiment
    • Bolting: Presence (1) or absence (0) of inflorescence 2 wk after the start of experiment
    • BranchNo: No. of reproductive branches. Dead plants are considered zero numbers.
  • subsetSNP.py\
    Python script to extract genotype data from .hdf available at AraGWAS Catalog (https://aragwas.1001genomes.org).

  • reshapeSNP.R\
    R script to prepare genotype data for for "BranchNo2019GWAS.R" after subsetSNP.py.

  • BranchNo2019GWAS.R\
    Field GWAS of the branch number using 199 accessions of A. thaliana.

  • BranchNo2019GWASfigure.R\
    Manhattan and QQ plots for the output from "BranchNo2019GWAS.R".

simulation

This directory includes source codes for simulations, numerical examples, and their figure presentation, proceeding as follows.

Ising model simulation

  • IsingGenoWeighted.R\ R script for the forward Ising simulation when transition probabilities follow Mendelian inheritance.

Fitness function

  • AsymFDSfitnessFunctions.R\ Numerical analysis and figure presentation for fitness functions in response to allele frequency.

GWAS simulation

  • SLiM codes (xxxx.slim) for forward genetic simulations of negative frequency-dependent selection (NFDS); positive frequency-dependent selection (PFDS); overdominance (OD); and spatiotemporally varying selection (STVS).
  • outputSLiM_and_GWAS.R GWAS simulation and power analysis using the SLiM output.
  • outputFigures.R\ Figure presentations for the GWAS simulation.
  • /output\ A folder to save SLiM and R outputs, including the following input data for the vignette (index.Rmd)
    • toy1.rds: Single-locus example data with split subpopulations
    • toy2.rds: Single-locus example data with continuous space
    • toy3.rds: GWAS example with with split subpopulations
    • toy4.rds: GWAS example with with continuous space

Vignette

  • toydata.R\ R script to create input toy data for index.Rmd in the top directory

Funding

Japan Society for the Promotion of Science, Award: 20K15880

Japan Science and Technology Agency, Award: JPMJPR17Q4

Japan Science and Technology Agency, Award: JPMJCR16O3

Swiss National Science Foundation, Award: 31003A_182318

Swiss National Science Foundation, Award: 31003A_212551

University of Zurich, Award: URPP Global Change and Biodiversity